PD_scRNAseq: Monocle3 DGE & Modules

Author

Brian M. Schilder, Bioinformatician II

Setup

start_time <- Sys.time() 

#### #### Load Objects & Functions #### #### 
######################################################
# Import functions
root = "./"
source(file.path(root,"MAIN.R"))
import_parameters(params)

## **** __Utilized Cores__ **** = 2$subsetGenes
## [1] "protein_coding"
## 
## $subsetCells
## [1] 500
## 
## $resolution
## [1] 0.6
## 
## $resultsPath
## [1] "./Results"
## 
## $nCores
## [1] 2
## 
## $perplexity
## [1] 30

######################################################


#### #### PACKAGES #### #### 
###################################################### 
library(monocle3)
paste("monocle3", packageVersion("monocle3"))  

## [1] "monocle3 0.1.2"

library(cowplot) 
library(ggplot2)
library(dplyr) 
library(data.table) 
library(readxl) 
library(reshape2)
library(ggrepel)
library(plotly)
library(GeneOverlap) # BiocManager::install("GeneOverlap") 
######################################################


# Exporting 3D plots 
knitr::knit_hooks$set(webgl = rgl::hook_webgl)
dge_limit <- F # 100
nCores <- 4#parallel::detectCores()
set.seed(2019)

# Load preprocessed data
load("./Data/monocle3_CDS.RData")
cds

## class: cell_data_set 
## dim: 14827 19144 
## metadata(1): cds_version
## assays(1): counts
## rownames(14827): A1BG A2M ... ZZEF1 ZZZ3
## rowData names(1): gene_short_name
## colnames(19144): GTCATTTTCCGCATCT ATAAGAGAGGAGTTGC ...
##   CAGTAACCCGCTCGCA ACACCGGTCCGGCCCA
## colData names(23): nGene nUMI ... cell_type cluster_ext_type
## reducedDimNames(2): PCA UMAP
## spikeNames(0):

Differential Gene Expression

Detect whether or not to limit DGE analysis to only the top N most variable genes.

# DGE limiter
# dge_limit <- 10
if(dge_limit){
  cds_DGE <- cds[var.genes[1:dge_limit],]
  print(paste0("Testing only the top ",dge_limit," most variable genes in DGE analysis."))
} else{
  cds_DGE <- cds
  print(paste0("Testing all ",dim(cds)[1]," genes in DGE analysis."))
} 

## [1] "Testing all 14827 genes in DGE analysis."

# DGE distribution type
expression_family <- "quasipoisson" # Recommended by Monocle3 authors for most cases.
within_clusters <- c(1,2)
DT_max <- 1000

Across Clusters

PD vs. Control

dge.dx <- monocle3_DGE(cds_DGE = cds_DGE,
                           variable = "dx",
                           nCores = nCores, 
                           expression_family = expression_family,
                           plot_volcano = T, 
                           results_path = "./Results/across_PD.vs.Ctrl.csv") 

## [1] "Results file already exists. Importing..."

## Warning: Removed 1197 rows containing missing values (geom_point).

createDT(head(dge.dx, DT_max))

PD vs. GBA

dge.mut <- monocle3_DGE(cds_DGE = cds_DGE,
                           variable = "dx",
                           variable_subsets = c("PD","GBA"),
                           nCores = nCores, 
                           expression_family = expression_family,
                           plot_volcano = T, 
                           results_path = "./Results/across_PD.vs.GBA.csv") 

## [1] "Results file already exists. Importing..."

## Warning in max(neg.log.pvals): no non-missing arguments to max; returning -
## Inf

## Warning in max(abs(dge_sig$estimate)): no non-missing arguments to max;
## returning -Inf

## Warning: Removed 6 rows containing missing values (geom_text_repel).

## Warning: Removed 1 rows containing missing values (geom_hline).

createDT(head(dge.mut, DT_max))

Canonical vs. Intermediate Monocytes

• Compare all Canonical Monocytes (Cluster 1) to all Intermediate Monocytes (Cluster 2), regardless of disease status.

dge.clust <- monocle3_DGE(cds_DGE = cds_DGE,
                          variable = "Cluster", 
                          variable_subsets = c(1,2),
                          nCores = nCores, 
                          expression_family = expression_family,
                          plot_volcano = T, 
                          results_path = "./Results/across_Clust1.vs.Clust2.csv") 

## [1] "Results file already exists. Importing..."

## Warning: Removed 2527 rows containing missing values (geom_point).

createDT(head(dge.clust, DT_max))

Within Clusters

Disease

• Compare PD vs. Controls DGE within each cluster.
  
• Cluster 1 = Canonical Monocytes, Cluster 2 = Intermediate Monocytes.

for (clust in within_clusters){
  cat("\n")
  cat("#### Cluster ",clust,"\n")
  cds_clust <- cds_DGE[,pData(cds_DGE)$Cluster==clust]
  dge.dx.clust <- monocle3_DGE(cds_DGE = cds_clust,
                           variable = "dx",  
                           nCores = nCores, 
                           expression_family = expression_family,
                           plot_volcano = T, 
                           results_path = paste0("./Results/within.Clust",clust,"_PD.vs.Ctrl.csv") )
  createDT_html(head(dge.dx.clust, DT_max)) %>% print()
  cat("\n")
}

Cluster 1

[1] “Results file already exists. Importing…”

## Warning: Removed 1419 rows containing missing values (geom_point).

Cluster 2

[1] “Results file already exists. Importing…”

## Warning: Removed 1957 rows containing missing values (geom_point).

PD vs. GBA

for (clust in within_clusters){
  cat("\n")
  cat("#### Cluster ",clust,"\n")
  cds_clust <- cds_DGE[,(pData(cds_DGE)$Cluster==clust)]
  dge.mut.clust <- monocle3_DGE(cds_DGE = cds_clust,
                           variable = "mut",  
                           variable_subsets = c("PD","GBA"),
                           nCores = nCores, 
                           expression_family = expression_family,
                           plot_volcano = T, 
                           results_path = paste0("./Results/within.Clust",clust,"_PD.vs.GBA.csv") ) 
  createDT_html(head(dge.mut.clust, DT_max)) %>% print()
  cat("\n")
}

Cluster 1

[1] “Results file already exists. Importing…”

## Warning: Removed 1775 rows containing missing values (geom_point).

Cluster 2

[1] “Results file already exists. Importing…”

## Warning: Removed 2359 rows containing missing values (geom_point).

Top Specific Markers

• Find genes that characterize each cluster.
  
• Specifically, only compare Clusters 1 and 2 to focuses on differences between these.

# Cluster-specific markers
marker_res <- top_cluster_markers(cds_DGE, 
                                  cluster_list=c(1,2), 
                                  genes_to_test_per_group=1000, 
                                  save_path= "./Results/cluster_markers.csv")

filtered_res <- marker_res %>%
    filter(fraction_expressing >= 0.10 & marker_test_q_value <=0.05) %>%
    group_by(cell_group) %>% arrange(desc(specificity), desc(pseudo_R2))
createDT(filtered_res)

## Warning in instance$preRenderHook(instance): It seems your data is too
## big for client-side DataTables. You may consider server-side processing:
## https://rstudio.github.io/DT/server.html